Overview

Dataset statistics

Number of variables8
Number of observations9000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory562.6 KiB
Average record size in memory64.0 B

Variable types

TimeSeries8

Timeseries statistics

Number of series8
Time series length9000
Starting point0
Ending point8999
Period1
2024-04-12T23:59:27.117450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:27.482652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Alerts

dht_air_humidity is highly overall correlated with dht_ground_humidity and 1 other fieldsHigh correlation
dht_air_temperature is highly overall correlated with dht_ground_humidity and 2 other fieldsHigh correlation
dht_ground_humidity is highly overall correlated with dht_air_humidity and 2 other fieldsHigh correlation
dht_ground_temperature is highly overall correlated with dht_air_temperatureHigh correlation
light_air is highly overall correlated with light_groundHigh correlation
light_ground is highly overall correlated with light_airHigh correlation
timestamp is highly overall correlated with dht_air_humidity and 2 other fieldsHigh correlation
timestamp is non stationaryNon stationary
light_air is non stationaryNon stationary
light_ground is non stationaryNon stationary
moisture_ground is non stationaryNon stationary
dht_air_humidity is non stationaryNon stationary
dht_air_temperature is non stationaryNon stationary
dht_ground_humidity is non stationaryNon stationary
dht_ground_temperature is non stationaryNon stationary
light_air is seasonalSeasonal
light_ground is seasonalSeasonal
moisture_ground is seasonalSeasonal
dht_air_humidity is seasonalSeasonal
dht_air_temperature is seasonalSeasonal
dht_ground_humidity is seasonalSeasonal
dht_ground_temperature is seasonalSeasonal
timestamp has unique valuesUnique
light_air has 5015 (55.7%) zerosZeros
light_ground has 3597 (40.0%) zerosZeros

Reproduction

Analysis started2024-04-12 21:59:02.108668
Analysis finished2024-04-12 21:59:26.955627
Duration24.85 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

timestamp
Numeric time series

HIGH CORRELATION  NON STATIONARY  UNIQUE 

Distinct9000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6972166 × 109
Minimum1.6970634 × 109
Maximum1.6973758 × 109
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2024-04-12T23:59:27.922574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.6970634 × 109
5-th percentile1.6970789 × 109
Q11.6971403 × 109
median1.697215 × 109
Q31.6972896 × 109
95-th percentile1.6973593 × 109
Maximum1.6973758 × 109
Range312468
Interquartile range (IQR)149314.5

Descriptive statistics

Standard deviation89028.741
Coefficient of variation (CV)5.2455732 × 10-5
Kurtosis-1.1500781
Mean1.6972166 × 109
Median Absolute Deviation (MAD)74665.5
Skewness0.057722304
Sum1.527495 × 1013
Variance7.9261167 × 109
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9981966536
2024-04-12T23:59:28.243433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-12T23:59:29.892969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:30.039245image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1697063380 1
 
< 0.1%
1697264685 1
 
< 0.1%
1697264481 1
 
< 0.1%
1697264515 1
 
< 0.1%
1697264548 1
 
< 0.1%
1697264581 1
 
< 0.1%
1697264616 1
 
< 0.1%
1697264649 1
 
< 0.1%
1697264718 1
 
< 0.1%
1697264415 1
 
< 0.1%
Other values (8990) 8990
99.9%
ValueCountFrequency (%)
1697063380 1
< 0.1%
1697063416 1
< 0.1%
1697063450 1
< 0.1%
1697063484 1
< 0.1%
1697063518 1
< 0.1%
1697063553 1
< 0.1%
1697063587 1
< 0.1%
1697063619 1
< 0.1%
1697063652 1
< 0.1%
1697063685 1
< 0.1%
ValueCountFrequency (%)
1697375848 1
< 0.1%
1697375815 1
< 0.1%
1697375779 1
< 0.1%
1697375745 1
< 0.1%
1697375712 1
< 0.1%
1697375678 1
< 0.1%
1697375644 1
< 0.1%
1697375600 1
< 0.1%
1697375567 1
< 0.1%
1697375528 1
< 0.1%
2024-04-12T23:59:29.288904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

light_air
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1660
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.29311
Minimum0
Maximum2736
Zeros5015
Zeros (%)55.7%
Memory size70.4 KiB
2024-04-12T23:59:30.283368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3944
95-th percentile1911.35
Maximum2736
Range2736
Interquartile range (IQR)944

Descriptive statistics

Standard deviation715.04248
Coefficient of variation (CV)1.4123093
Kurtosis0.54397794
Mean506.29311
Median Absolute Deviation (MAD)0
Skewness1.2487838
Sum4556638
Variance511285.75
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.02039248909
2024-04-12T23:59:30.555860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-12T23:59:32.594233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:32.744818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 5015
55.7%
1600 20
 
0.2%
1072 17
 
0.2%
80 14
 
0.2%
912 14
 
0.2%
1535 14
 
0.2%
592 13
 
0.1%
1424 13
 
0.1%
1603 12
 
0.1%
1520 12
 
0.1%
Other values (1650) 3856
42.8%
ValueCountFrequency (%)
0 5015
55.7%
1 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
2736 1
 
< 0.1%
2732 2
 
< 0.1%
2731 1
 
< 0.1%
2730 2
 
< 0.1%
2727 1
 
< 0.1%
2725 5
0.1%
2722 1
 
< 0.1%
2721 3
< 0.1%
2715 1
 
< 0.1%
2714 3
< 0.1%
2024-04-12T23:59:32.073977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

light_ground
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1946
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947.46078
Minimum0
Maximum3568
Zeros3597
Zeros (%)40.0%
Memory size70.4 KiB
2024-04-12T23:59:33.072554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q32114
95-th percentile2879
Maximum3568
Range3568
Interquartile range (IQR)2114

Descriptive statistics

Standard deviation1148.4086
Coefficient of variation (CV)1.212091
Kurtosis-1.2941499
Mean947.46078
Median Absolute Deviation (MAD)13
Skewness0.60852967
Sum8527147
Variance1318842.4
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2689754405
2024-04-12T23:59:33.444198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-12T23:59:35.174804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:35.312495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 3597
40.0%
1 227
 
2.5%
2 188
 
2.1%
3 138
 
1.5%
16 121
 
1.3%
6 56
 
0.6%
5 55
 
0.6%
4 50
 
0.6%
2096 44
 
0.5%
7 43
 
0.5%
Other values (1936) 4481
49.8%
ValueCountFrequency (%)
0 3597
40.0%
1 227
 
2.5%
2 188
 
2.1%
3 138
 
1.5%
4 50
 
0.6%
5 55
 
0.6%
6 56
 
0.6%
7 43
 
0.5%
8 11
 
0.1%
9 31
 
0.3%
ValueCountFrequency (%)
3568 1
< 0.1%
3493 1
< 0.1%
3492 1
< 0.1%
3491 1
< 0.1%
3490 2
< 0.1%
3488 1
< 0.1%
3487 2
< 0.1%
3486 1
< 0.1%
3485 1
< 0.1%
3482 1
< 0.1%
2024-04-12T23:59:34.494000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

moisture_ground
Numeric time series

NON STATIONARY  SEASONAL 

Distinct1455
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1604.9336
Minimum0
Maximum2161
Zeros32
Zeros (%)0.4%
Memory size70.4 KiB
2024-04-12T23:59:35.644373image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile854
Q11489
median1666
Q31872
95-th percentile2026
Maximum2161
Range2161
Interquartile range (IQR)383

Descriptive statistics

Standard deviation380.22386
Coefficient of variation (CV)0.23690941
Kurtosis3.7961341
Mean1604.9336
Median Absolute Deviation (MAD)196
Skewness-1.7092623
Sum14444402
Variance144570.18
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.535678426 × 10-7
2024-04-12T23:59:36.082569image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-12T23:59:43.616536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:44.006578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1520 94
 
1.0%
1535 91
 
1.0%
1552 91
 
1.0%
1936 73
 
0.8%
1840 66
 
0.7%
1872 62
 
0.7%
1904 57
 
0.6%
1808 49
 
0.5%
1856 39
 
0.4%
1584 38
 
0.4%
Other values (1445) 8340
92.7%
ValueCountFrequency (%)
0 32
0.4%
1 1
 
< 0.1%
2 1
 
< 0.1%
11 1
 
< 0.1%
13 2
 
< 0.1%
15 1
 
< 0.1%
16 3
 
< 0.1%
21 1
 
< 0.1%
23 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
2161 2
< 0.1%
2160 1
 
< 0.1%
2159 2
< 0.1%
2155 3
< 0.1%
2154 1
 
< 0.1%
2151 1
 
< 0.1%
2149 1
 
< 0.1%
2145 1
 
< 0.1%
2144 4
< 0.1%
2140 1
 
< 0.1%
2024-04-12T23:59:42.424365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dht_air_humidity
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.674
Minimum31
Maximum59
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2024-04-12T23:59:44.789579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile34
Q136
median45
Q357
95-th percentile58
Maximum59
Range28
Interquartile range (IQR)21

Descriptive statistics

Standard deviation9.6793664
Coefficient of variation (CV)0.2119229
Kurtosis-1.6874392
Mean45.674
Median Absolute Deviation (MAD)10
Skewness0.1542927
Sum411066
Variance93.690134
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9055436408
2024-04-12T23:59:45.434315image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
2024-04-12T23:59:48.455692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:48.755634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
58 1159
12.9%
35 1074
11.9%
37 780
 
8.7%
57 764
 
8.5%
36 576
 
6.4%
56 546
 
6.1%
55 533
 
5.9%
45 527
 
5.9%
39 386
 
4.3%
34 382
 
4.2%
Other values (19) 2273
25.3%
ValueCountFrequency (%)
31 36
 
0.4%
32 174
 
1.9%
33 81
 
0.9%
34 382
 
4.2%
35 1074
11.9%
36 576
6.4%
37 780
8.7%
38 289
 
3.2%
39 386
 
4.3%
40 232
 
2.6%
ValueCountFrequency (%)
59 340
 
3.8%
58 1159
12.9%
57 764
8.5%
56 546
6.1%
55 533
5.9%
54 150
 
1.7%
53 54
 
0.6%
52 34
 
0.4%
51 21
 
0.2%
50 11
 
0.1%
2024-04-12T23:59:47.374239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dht_air_temperature
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.805978
Minimum20.2
Maximum25.8
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2024-04-12T23:59:49.356243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum20.2
5-th percentile21.6
Q122.1
median22.7
Q323.4
95-th percentile24.2
Maximum25.8
Range5.6
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation0.9193466
Coefficient of variation (CV)0.04031165
Kurtosis0.077134087
Mean22.805978
Median Absolute Deviation (MAD)0.7
Skewness0.42456098
Sum205253.8
Variance0.84519818
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.06571204902
2024-04-12T23:59:50.041260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-12T23:59:53.072623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:53.377563image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
23 512
 
5.7%
21.7 498
 
5.5%
21.8 457
 
5.1%
22.5 415
 
4.6%
22.9 402
 
4.5%
22.8 401
 
4.5%
23.1 395
 
4.4%
22.3 375
 
4.2%
21.9 365
 
4.1%
22.7 361
 
4.0%
Other values (47) 4819
53.5%
ValueCountFrequency (%)
20.2 10
 
0.1%
20.3 10
 
0.1%
20.4 9
 
0.1%
20.5 10
 
0.1%
20.6 7
 
0.1%
20.7 14
0.2%
20.8 28
0.3%
20.9 21
0.2%
21 18
0.2%
21.1 24
0.3%
ValueCountFrequency (%)
25.8 32
0.4%
25.7 17
0.2%
25.6 16
0.2%
25.5 14
0.2%
25.4 17
0.2%
25.3 16
0.2%
25.2 14
0.2%
25.1 17
0.2%
25 14
0.2%
24.9 21
0.2%
2024-04-12T23:59:51.955319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dht_ground_humidity
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.618889
Minimum31
Maximum65
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2024-04-12T23:59:54.012826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile36
Q138
median44
Q359
95-th percentile63
Maximum65
Range34
Interquartile range (IQR)21

Descriptive statistics

Standard deviation10.399353
Coefficient of variation (CV)0.21838715
Kurtosis-1.6315051
Mean47.618889
Median Absolute Deviation (MAD)8
Skewness0.26626366
Sum428570
Variance108.14655
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.8416900958
2024-04-12T23:59:54.742800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
2024-04-12T23:59:57.474088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-12T23:59:57.794189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
38 1164
12.9%
59 805
 
8.9%
37 702
 
7.8%
62 657
 
7.3%
58 657
 
7.3%
36 646
 
7.2%
39 465
 
5.2%
40 382
 
4.2%
44 376
 
4.2%
43 362
 
4.0%
Other values (25) 2784
30.9%
ValueCountFrequency (%)
31 27
 
0.3%
32 69
 
0.8%
33 35
 
0.4%
34 84
 
0.9%
35 104
 
1.2%
36 646
7.2%
37 702
7.8%
38 1164
12.9%
39 465
 
5.2%
40 382
 
4.2%
ValueCountFrequency (%)
65 42
 
0.5%
64 78
 
0.9%
63 353
3.9%
62 657
7.3%
61 214
 
2.4%
60 339
3.8%
59 805
8.9%
58 657
7.3%
57 166
 
1.8%
56 78
 
0.9%
2024-04-12T23:59:56.366119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dht_ground_temperature
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct71
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.181189
Minimum18.3
Maximum25.8
Zeros0
Zeros (%)0.0%
Memory size70.4 KiB
2024-04-12T23:59:58.424963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18.3
5-th percentile20.3
Q121.1
median22.2
Q323.2
95-th percentile24.1
Maximum25.8
Range7.5
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.2684739
Coefficient of variation (CV)0.057186921
Kurtosis-0.58078213
Mean22.181189
Median Absolute Deviation (MAD)1
Skewness0.18119503
Sum199630.7
Variance1.609026
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.0597276072
2024-04-12T23:59:59.215323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-04-13T00:00:04.083645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2024-04-13T00:00:04.452088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
23.7 493
 
5.5%
22.8 346
 
3.8%
22.7 338
 
3.8%
21.8 337
 
3.7%
22.9 313
 
3.5%
21.4 295
 
3.3%
21.1 292
 
3.2%
21.3 268
 
3.0%
23.8 263
 
2.9%
22.1 258
 
2.9%
Other values (61) 5797
64.4%
ValueCountFrequency (%)
18.3 1
 
< 0.1%
18.4 1
 
< 0.1%
18.5 1
 
< 0.1%
18.8 2
 
< 0.1%
19.2 1
 
< 0.1%
19.3 1
 
< 0.1%
19.4 8
 
0.1%
19.5 16
0.2%
19.6 21
0.2%
19.7 25
0.3%
ValueCountFrequency (%)
25.8 29
0.3%
25.7 21
0.2%
25.6 26
0.3%
25.5 19
0.2%
25.4 30
0.3%
25.3 8
 
0.1%
25.2 7
 
0.1%
25.1 7
 
0.1%
25 8
 
0.1%
24.9 16
0.2%
2024-04-13T00:00:02.555585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Interactions

2024-04-12T23:59:23.805286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:05.654018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:07.780867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:10.902743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:14.073709image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:16.405694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:18.642883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:21.063580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:24.112920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:05.882557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:08.213776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:11.325593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:14.464613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:16.692560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:18.894227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:21.564483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:24.372714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:06.062900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:08.644951image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:11.682149image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:14.743004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:16.996725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:19.214751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:21.846513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:24.644614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:06.310406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:09.094633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:12.094669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:15.052905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:17.312099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:19.482615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:22.238974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:24.864494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:06.532392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:09.442873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:12.522382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:15.314936image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:17.575287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:19.714255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:22.577207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:25.343037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:06.764053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:09.785561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:12.953501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:15.592676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:17.844442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:19.955699image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:22.883133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:25.602821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:06.983142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:10.082767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:13.303042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:15.864511image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:18.105299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:20.222131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:23.162334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:25.924710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:07.260233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:10.356508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:13.613650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:16.117448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:18.364906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:20.747129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-12T23:59:23.442640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-04-13T00:00:04.903110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dht_air_humiditydht_air_temperaturedht_ground_humiditydht_ground_temperaturelight_airlight_groundmoisture_groundtimestamp
dht_air_humidity1.000-0.4020.932-0.267-0.151-0.037-0.1630.717
dht_air_temperature-0.4021.000-0.5470.9590.026-0.039-0.115-0.613
dht_ground_humidity0.932-0.5471.000-0.435-0.227-0.131-0.1870.701
dht_ground_temperature-0.2670.959-0.4351.0000.1100.079-0.119-0.470
light_air-0.1510.026-0.2270.1101.0000.9160.086-0.042
light_ground-0.037-0.039-0.1310.0790.9161.0000.2500.141
moisture_ground-0.163-0.115-0.187-0.1190.0860.2501.0000.221
timestamp0.717-0.6130.701-0.470-0.0420.1410.2211.000

Missing values

2024-04-12T23:59:26.274354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-12T23:59:26.672728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestamplight_airlight_groundmoisture_grounddht_air_humiditydht_air_temperaturedht_ground_humiditydht_ground_temperature
01697063380231119999444024.44024.3
11697063416232520069294024.34024.3
21697063450232020039524024.34024.3
31697063484232919999504024.34024.2
41697063518230719839614024.34024.2
51697063553233019859614024.34024.2
61697063587232119989314024.34024.1
71697063619231619779424024.34024.1
81697063652231119809664024.34024.1
91697063685231719839764124.34024.1
timestamplight_airlight_groundmoisture_grounddht_air_humiditydht_air_temperaturedht_ground_humiditydht_ground_temperature
89901697375528723251913215721.95821.8
89911697375567714251212335721.95821.8
89921697375600713251714245721.95821.8
89931697375644695252713195721.95821.8
89941697375678693250513585721.95821.8
89951697375712678246512495721.95821.8
89961697375745655241912325721.95821.8
8997169737577962924102065721.95821.8
89981697375815661235213515721.95821.8
89991697375848639234012325721.95821.8